Ranjit Paul1), Asmaa Mohamed2), Peren Canatalay3), Ashima Kukkar4), Sadiq Hussain1), Arun Baruah1), Jiten Hazarika1), Silvia Gaftandzhieva5), Esraa Mahareek2), Abeer Desuky 2), Rositsa Doneva5)
1)Dibrugarh University, Dibrugarh (India)
2)Al-Azhar University (girls branch), Cairo (Egypt)
3)Istinye University, Istanbul (Turkey)
4)Chitkara University, Punjab (India)
5)University of Plovdiv “Paisii Hilendarski”, Plovdiv (Bulgaria)
https://doi.org/10.53656/math2025-6-4-obc
Abstract. The paper proposes a comprehensive student academic performance prediction approach by integrating machine learning with metaheuristic optimization. Initial models (Logistic Regression, Decision Tree, Random Forest, MLP) were refined using boosting techniques (Gradient Boosting, XGBoost, LightGBM), with XGBoost achieving 95.59% accuracy. Eight modern optimization algorithms were applied for feature selection to enhance model efficiency and interpretability, with the Grey Wolf Optimizer and the Heap-Based Optimizer outperforming others in key metrics. Support
Vector Machine algorithms applied after feature selection strengthened the predictive capability of the selected feature subsets. The research outcomes demonstrate that uniting boosting approaches with feature selection algorithms enables the creation of reliable and scalable predictive models that detect student success and failure earlier.
Keywords: Machine Learning, Optimization Algorithms, Educational Data Mining, Ensemble Models, Boosting Algorithms.
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